Generation of suggestions for consumption of content typically is based on heterogeneous segments of consumers. Certain service providers have utilized online methods for generating such suggestions based on, for example, collection of data related to a user's recent consumption of content assets (e.g., two recently viewed movies) and rating thereof according to a fixed scale (such as a scale ranging from one to five). Aggregated or merely cumulative data are then compared against either a database or other users within a specific consumer segment to generate a list of suggestions. In addition or in the alternative, polling of consumers (e.g., a voting solution, such as voting for two content assets of value) generally rely on comparisons against other user's selection of content assets in order to create viewing suggestions. Yet, such customization of content largely fails to incorporate personalized active consumption trends.